Poster (Health Services, Economics and Policy Change) ID 1969166
Bibliographic record
Abstract
Background We studied the feasibility of a home-based screening sleep test (HBSST), the validity of four questionnaires used to screen for sleep-related breathing disorders (SRBDs), and the potential association between SRBD and clinical features in individuals with spinal cord injury (SCI). Methods Adults with subacute/chronic (>1 month post-injury) SCI were recruited for the cross-sectional study and qualitative analysis. Feasibility of the HBSST was objectively evaluated and participants shared their experience. We also examined the validity of the Berlin, STOP, Medical Outcomes Study Sleep Scale [MOS-SS], and STOP-Bang screening questionnaires. We investigated the association between the degree of SRBD and three features (i.e., neck circumference, body mass index [BMI] and oropharynx opening as assessed using the Modified Mallampati classification [MMC]). Results There were 13 females and 18 males with ages varying from 20 to 86 years (mean age: 54.7 years) with motor complete (n=8) or incomplete SCI at cervical (n=21) or thoraco-lumbar levels. Time since SCI varied from 1.5 to 474 months. Overall, 28 individuals completed the HBSST and endorsed its feasibility. Mean apnea-hypopnea index (AHI) was 17.3 events/hour (range: 0.5-83.7). AHI was significantly correlated with Berlin (p=0.036) and STOP-Bang scores (p=0.009). There was no significant correlation between AHI and MOS-SS (p=0.348) or STOP (p=0.165). AHI was not associated with neck circumference (p=0.614), BMI (p=0.958), or MMC (p=0.335). Conclusions Our results suggest that HBSST is a feasible screening method, and Berlin and STOP-Bang are valid screening questionnaires for the SCI population. AHI was not correlated with BMI, neck circumference, or MMC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.893 | 0.696 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".